Pretend you buy one lottery ticket per week. However, you have a choice to two types. One type tells you immediately if you have won or lost, the other does not. For either type, if you win you receive your prize the following week, and cost per ticket, probability of winning, and size of the payoff are the same. There has been enough research done to predict that you will buy the ticket with immediate feedback, that is, make an observing response to obtain stimuli which signal reward and nonreward. For the last sixteen months I have been developing further a mathematical model which accounts for the preference for signalled events, and have also determined the interaction of a number of variables on their influence of the size of the preference. I had indicated in the original grant proposal that "it is possible that some of the experiments will not be entirely completed during the two-year period," (p. 45). Although I have completed sixteen studies, five more remain to be done, and I thus need the current grant to be continued. The objectives for the next two years are: to test the accuracy of the mathematical model in additional appetitive learning situations (it is currently successful in 35 different areas); to complete testing the effect of 10 additional variables on the preference for signalled events; and to extend the work done to humans, to determine if the mathematical model could be used in clinical situations to help people decrease the aversiveness of failure (nonreward). The model which I have developed is based on the Rescorla-Wagner model, but assumes that both larager and smaller than expected rewards are surprising and are the basis of conditioning. It predicts acquisition of the preference for signalled events because signalled nonreward is less aversive than unsignalled nonreward. If correct, the implication is clear: transform unpredictable situations into predictable ones to reduce the aversiveness of situations where nonreward or failure is unavoidable. The model makes clearcut predictions for the best conditions to reduce the aversiveness of nonreward.